Trustworthy AI in Cardiology

How can a clinician decide whether an AI tool is trustworthy and clinically useful?

AI is already embedded in cardiology. Algorithms can measure ventricular function, flag abnormal ECGs, segment cardiac CT scans, identify patients at risk of deterioration and generate draft clinical documentation. Producing an answer, however, is not the same as producing a trustworthy one.

Before an AI tool enters clinical care, clinicians need to know more than its headline accuracy. Does it work outside the population in which it was developed? Does it fit the local workflow? What happens when it is wrong? Who remains responsible for the decision, and does using it actually improve patient care?

This article distils the practical themes from the Medmastery webinar Trustworthy AI in Cardiac Imaging and Cardiology.

Does the AI model work beyond its development dataset?

An AI model may perform well on the data used to build it and still fail when transferred to another hospital, patient population or clinical pathway. Clinicians should look for independent external validation, not merely an internal test set drawn from the same source as the training data.

Performance should also be examined across clinically relevant subgroups. An ECG algorithm trained predominantly on North American data, for example, may not retain the same calibration or diagnostic performance in another population. Differences in disease prevalence, referral patterns, equipment and data quality can all alter the result.

Sensitivity and specificity are only part of the picture. Predictive values, calibration, false-positive burden, uninterpretable studies and performance at the intended decision threshold may matter more at the bedside.

Clinical validity is not clinical utility

An algorithm can accurately identify an imaging feature without improving care. The more important questions are whether its output changes clinical decisions, shortens time to treatment, prevents unnecessary investigation or improves patient outcomes.

Ideally, evaluation should progress from retrospective technical validation to prospective testing within the clinical workflow. Once deployed, performance also requires surveillance as patient populations change, software is updated and clinical practice evolves.

Context is key

AI tools are not context-free diagnostic machines. Their performance depends on where, how and by whom they are used. Before adoption, you should ask:

• Which patients will be assessed?
• What data or images are required?
• How often will the model fail or abstain?
• Who reviews its output?
• What action follows a positive result?
• Can the existing service absorb the additional referrals or investigations it generates?

A tool that works in a specialist cardiac imaging centre may create little benefit, or create substantial extra work, in a different setting.

The risks of using AI

Clinical AI may introduce false reassurance, false alarms, automation bias and unclear accountability. Errors may introduce dogma which becomes systemic. A single clinician may make an isolated mistake, however a poorly calibrated algorithm can repeat the same mistake across thousands of patients.

Governance needs to define who selects the tool, who monitors its performance, who may override it and who remains accountable for the clinical decision. Regulatory approval alone does not establish local suitability.

The risks of not using AI

Caution should not become therapeutic inertia. AI may improve case finding, reduce repetitive measurement and identify patterns that clinicians cannot reliably detect. Delaying a useful tool may also have consequences.

The comparison should be ‘AI-assisted care’ versus the current pathway, measured through clinically clinical outcomes, workload, cost and the potential for unintended harm.

Efficiency may create more work

Faster or cheaper detection can increase demand rather than reduce it. If AI makes screening easier, more patients may be screened, more abnormalities may be identified and more people may require confirmatory testing or specialist review.

So, the relevant measure is not simply the time saved on one scan or report but the effect on the whole pathway from identification to investigation, treatment and follow-up.

Measuring real value

The value of an AI tool depends on the outcomes achieved relative to the resources required. Accuracy is an intermediate endpoint, not the final one. A clinically valuable tool should ideally do one or more of the following:

• improve patient outcomes;
• shorten time to diagnosis or treatment;
• reduce unnecessary tests;
• decrease clinically meaningful workload;
• improve access or equity;
• achieve these benefits without introducing disproportionate harm or cost.

Bottom line

Clinicians do not need to understand how the AI algorithm works, but do need to understand the evidence supporting its use. Before trusting an AI tool, ask whether it has been independently validated, if it works in the intended population, and how failure will be detected and managed. Most importantly does the adjunctive tool improve patient care rather than merely producing another result.

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References

• Wiesbauer F. Trustworthy AI in Cardiac Imaging and Cardiology  Medmastery
• Wiesbauer F. AI for Medical Education and Research LITFL
• Elshahati N. AI Scribes for Clinical Practice. LITFL
• Wiesbauer F. ChatGPT Essentials for Clinicians. Medmastery
• Guilleminot S. AI in Healthcare. LITFL